Flexible workshop production scheduling method based on adaptive local search

Abstract

The invention discloses an adaptive local search-based flexible workshop production scheduling method, which comprises the following steps of S1, acquiring workshop processing information, performing population initialization, and generating an initial solution; s2, decoding the initial population obtained in the step S1 by adopting an insertion method to generate an executable scheduling scheme; s3, judging whether population iteration in the step S2 is completed or not; s4, screening the populations which do not trigger the termination condition according to the judgment result of the step S3; s5, optimizing the population screened in the step S4 by adopting a self-adaptive variable-scale variation method, and updating the population by adopting a crossover and self-adaptive calculation variation mode number and variation probability in the self-adaptive variable-scale variation method to obtain a new offspring population; and S6, combining the population screened in the step S4 and the offspring obtained in the step S5 as a new population, and entering the next iteration.

Description

technical field [0001] The invention relates to the technical field of production scheduling, in particular to a flexible workshop production scheduling method with adaptive local search. Background technique [0002] Flexible job shop production scheduling refers to a decomposable job (such as machining), under the premise of meeting the constraints of the process route, production resources, delivery time and other constraints as much as possible, by arranging the resources used in its operation, The time required, the time point of execution and the sequence in order to obtain a work that is optimized for product manufacturing time or cost. The production scheduling problem of flexible job shop is a typical NP-hard problem, and it is impossible to find a polynomial-time algorithm to obtain the optimal solution accurately. Moreover, whether starting from the optimization problem itself or the actual needs of the application scenario, there is no need to obtain the optimal...

AI Technical Summary

Problems solved by technology

The production scheduling problem of the flexible job shop is a typical NP-hard problem, and it is impossible to find a polynomial time algorithm to accurately obtain the optimal solution

However, since the neighborhood structure selection in the variable neighborhood search algorithm uses a random method, it is impossible to adaptively select the variable neighborhood structure according to the optimization stage of the genetic algorithm and the situation of population diversity, so the present invention proposes a flexible local search algorithm Job shop production scheduling method, this method adjusts the variable neighborhood search method based on the current population sample diversity and iteration stage, and can perform local search adaptively to solve the premature optimization problem of flexible job shop production scheduling

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